from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					'''autoformer'''
 __A			       =					{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
     '''num_hidden_layers''': '''encoder_layers''',
 }







 def __init__(		self				:   Optional[Any]							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[int] = None							, lowercase_				:   str = "student_t"							, lowercase_				:   str = "nll"							, lowercase_				:   int = 1							, lowercase_				:   List[int] = [1, 2, 3, 4, 5, 6, 7]							, lowercase_				:   bool = True							, lowercase_				:   int = 0							, lowercase_				:   int = 0							, lowercase_				:   int = 0							, lowercase_				:   int = 0							, lowercase_				:   Optional[List[int]] = None							, lowercase_				:   Optional[List[int]] = None							, lowercase_				:   int = 64							, lowercase_				:   int = 2							, lowercase_				:   int = 2							, lowercase_				:   int = 2							, lowercase_				:   int = 2							, lowercase_				:   int = 32							, lowercase_				:   int = 32							, lowercase_				:   str = "gelu"							, lowercase_				:   float = 0.1							, lowercase_				:   float = 0.1							, lowercase_				:   float = 0.1							, lowercase_				:   float = 0.1							, lowercase_				:   float = 0.1							, lowercase_				:   int = 100							, lowercase_				:   float = 0.02							, lowercase_				:   bool = True							, lowercase_				:   Any=True							, lowercase_				:   int = 10							, lowercase_				:   int = 25							, lowercase_				:   int = 3							, **lowercase_				:   Any							, )						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    prediction_length
   _UpperCamelCase           =    context_length if context_length is not None else prediction_length
   _UpperCamelCase           =    distribution_output
   _UpperCamelCase           =    loss
   _UpperCamelCase           =    input_size
   _UpperCamelCase           =    num_time_features
   _UpperCamelCase           =    lags_sequence
   _UpperCamelCase           =    scaling
   _UpperCamelCase           =    num_dynamic_real_features
   _UpperCamelCase           =    num_static_real_features
   _UpperCamelCase           =    num_static_categorical_features
   if cardinality is not None and num_static_categorical_features > 0:
     if len(lowercase_) != num_static_categorical_features:
       raise ValueError(
           "The cardinality should be a list of the same length as `num_static_categorical_features`")
     _UpperCamelCase           =    cardinality
   else:
     _UpperCamelCase           =    [0]
   if embedding_dimension is not None and num_static_categorical_features > 0:
     if len(lowercase_) != num_static_categorical_features:
       raise ValueError(
           "The embedding dimension should be a list of the same length as `num_static_categorical_features`")
     _UpperCamelCase           =    embedding_dimension
   else:
     _UpperCamelCase           =    [min(50							, (cat + 1) // 2) for cat in self.cardinality]
   _UpperCamelCase           =    num_parallel_samples

   # Transformer architecture configuration
   _UpperCamelCase           =    input_size * len(self.lags_sequence) + self._number_of_features
   _UpperCamelCase           =    d_model
   _UpperCamelCase           =    encoder_attention_heads
   _UpperCamelCase           =    decoder_attention_heads
   _UpperCamelCase           =    encoder_ffn_dim
   _UpperCamelCase           =    decoder_ffn_dim
   _UpperCamelCase           =    encoder_layers
   _UpperCamelCase           =    decoder_layers

   _UpperCamelCase           =    dropout
   _UpperCamelCase           =    attention_dropout
   _UpperCamelCase           =    activation_dropout
   _UpperCamelCase           =    encoder_layerdrop
   _UpperCamelCase           =    decoder_layerdrop

   _UpperCamelCase           =    activation_function
   _UpperCamelCase           =    init_std

   _UpperCamelCase           =    use_cache

   # Autoformer
   _UpperCamelCase           =    label_length
   _UpperCamelCase           =    moving_average
   _UpperCamelCase           =    autocorrelation_factor

   super().__init__(is_encoder_decoder=lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   str)						->	int:


   """simple docstring"""







   return (
       sum(self.embedding_dimension)
       + self.num_dynamic_real_features
       + self.num_time_features
       + self.num_static_real_features
       + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
   )





def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




